Morphological distribution mapping: Utilisation of modelling to integrate particle size and shape distributions

Area of Science:

• Pharmaceutical Sciences
• Materials Science
• Chemical Engineering

Background:

• Understanding particle properties is crucial for pharmaceutical processing.
• Current methods may not fully capture the complex relationship between particle characteristics and behavior.
• A comprehensive approach is needed to link particle morphology to processing outcomes.

Purpose of the Study:

• To develop methods for utilizing whole particle distributions of size and shape parameters.
• To create a 'morphological distribution landscape' for a curated dataset.
• To improve the understanding of the particle landscape and its link to processing behavior.

Main Methods:

• A 1-dimensional principal component analysis (PCA) was applied.
• A dataset of imaged Active Pharmaceutical Ingredients (APIs), intermediates, and excipients was curated (2008-2022).
• Particle size, shape (elongation, length, width), and distribution shape data were analyzed.

Main Results:

• A 'morphological distribution landscape' was successfully created.
• The landscape allows differentiation of materials with equivalent size but varying shapes, and vice versa.
• The analysis focused on API samples within the curated dataset.

Conclusions:

• The morphological landscape offers a more complete understanding of particle properties.
• This approach can enhance the link between particle properties and pharmaceutical processing behavior.
• It enables leveraging historical data to identify and mitigate risks associated with materials of similar morphology.